The dissolved oxygen prediction method based on neural network
type: Item
instance of: scholarly article
title: The dissolved oxygen prediction method based on neural network
zbMATH Open document ID: 1377.93120
DOI: 10.1155/2017/4967870
author: Zhong Xiao
author: Lingxi Peng
author: Yi Chen
author: Haohuai Liu
author: Jiaqing Wang
author: Yangang Nie
published in: Complexity
publication date: 2018-01-05T00:00:00Z
review text: Summary: The dissolved oxygen (DO) is oxygen dissolved in water, which is an important factor for the aquaculture. Using BP neural network method with the combination of purelin, logsig, and tansig activation functions is proposed for the prediction of aquaculture's dissolved oxygen. The input layer, hidden layer, and output layer are introduced in detail including the weight adjustment process. The breeding data of three ponds in actual 10 consecutive days were used for experiments; these ponds were located in Beihai, Guangxi, a traditional aquaculture base in southern China. The data of the first 7 days are used for training, and the data of the latter 3 days are used for the test. Compared with the common prediction models, Curve Fitting (CF), AutoRegression (AR), Grey Model (GM), and Support Vector Machines (SVM), the experimental results show that the prediction accuracy of the neural network is the highest, and all the predicted values are less than 5\% of the error limit, which can meet the needs of practical applications, followed by AR, GM, SVM, and CF. The prediction model can help to improve the water quality monitoring level of aquaculture which will prevent the deterioration of water quality and the outbreak of disease.
Mathematics Subject Classification ID: 93C95
Mathematics Subject Classification ID: 93B20
Mathematics Subject Classification ID: 93E03
zbMATH DE Number: 6822313
zbMATH Keywords: aquaculture
zbMATH Keywords: dissolved oxygen (DO)
zbMATH Keywords: BP neural network
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/4967870
OpenAlex ID: W2763160615
cites work: Auto-regressive moving-average discrete-time dynamical systems and autocorrelation functions on real-valued Riemannian matrix manifolds
cites work: Grasping force based manipulation for multifingered hand-arm robot using neural networks
description: scientific article; zbMATH DE number 6822313